Currently automobiles use embedded sensors and computational powers for performance optimization. For better performance and maintenance knowing driver and his/her natural tendency i.e., unique driving style is important. For advanced driver assistance system identifying the driver is crucial. It is known that driver identification can be achieved to a good extent using few numbers of dedicated sensors. Such approach uses machine learning on data collected from a plurality of sensors. Since these are external sensors they add to cost and also deployment of many sensors increases operational and maintenance overhead. Thus such scheme has a huge logistics cost associated with it, which in turn limits the rapid and large scale deployment. Also installing and communicating with sensors requires additional overheads.